The World Wide Web is evolving rapidly and has, over the last couple of years, shifted from being a medium, in which information is mainly transmitted and consumed, into a platform where content is created, combined and shared. Using a regular browser, users may now make online presentations, write blogs, collaborate in real-time, or share their daily life using popular social networking services. In a way, social media services have become tremendously popular by having the abilities of connecting users and communities in a collaborative way. Social media services explore the opportunities for advanced communication and also serve as an advanced content sharing mechanism. Examples include not only social networking services (SNSs), such as MySpace and Facebook, but also telecom operator's services such as messaging, photo-sharing, person-to-person as well as conference calls and even microblogging services such as Twitter.
Millions of users are actively using these communication services, exposing new trends in communication and content sharing that has not been typical in the past. Therefore, the Web is becoming a globally distributed operating system connecting everyone and everything in the whole world. Accessing this network and learning from the data, makes it possible to harness collective intelligence of the users to build even better services and tools. Despite the large quantity and popularity of social media services, it is observed that users are overloaded with huge volumes of information with the huge number of communications tools, with the huge number of communication services and with becoming a part of a multi-million number user network with the corresponding risk of positioning oneself in unexpected communities.
Accordingly, a large challenge is posed to simplify communications using Web-based communication tools given the existence of information and contact overload. To address this problem, social recommender systems aim to improve the problem of information overload for communication services' users. Such systems attempt to present the most attractive, relevant, and trust-worthy content, often using personalization techniques adapted for the specific user. Moreover, new types of recommendations emerge within social media services, such as recommendations of people, communities, tools and services to connect to, to invite, or to proceed to for real-time collaborative tasks. Due to use of such services, a tremendous amount of social data is generated as continuous data streams (e.g., Twitter). With the new trend of communication services, social data (on-line interactions) is becoming much more accessible on the cloud through different on-line social media services (e.g., on-line social networks, blogs, calendars, emails, etc.).
Social networking analysis has been a quite active research area over the last few years. Different methods related to social community discovery have been discussed. Yet these methods are not enough to directly address the problem for discovering micro-communities based on real-time interactions logs. In most of the cases, the methods use static attributes of the contact individuals. For instance, interests or professions are used as context of identifying communities. Further, an article to Ankolekar et al., discusses the idea of finding the strongest communities based on communication history. Activity oriented social networking systems collect and aggregate real-time presence status and interest from different communication services. For example, CenseMe, a personal sensing system, automatically shares presence and activity information with the contacts of different social networks.
Apart from Google's page-rank algorithm, there are some new approaches for social search techniques which typically search over a social network and rank upon analyzing social data. In these techniques, tags, bookmarks and taggers (users) are exposed as social data. Aggregation of social networks (e.g., Del.icio.us and Flickr) and Web 2.0 applications improves accuracy in social search and recommendation systems. Integration may be possible from the service owner's perspective or even from the service user's perspective. In certain literature, users' profiles are integrated in the service provider, where the chances of privacy leakage are quite high.
Thus, it will be appreciated from the foregoing that there are a number of different social networking services in existence and are also many communication services (email, phone, SMS etc.). Each user has a set of “friends”, “buddies” or “contacts” for each service. There are also examples of aggregated contact lists that aggregate contacts from several sources (phone book, Facebook etc.) into one big contact list. These contact lists make it easier to contact the right person, or to respond to a contact attempt based on the fact that the person is in a user's contact list. However in such existing solutions there is no priority between contacts that would make contacting the right persons easier or would allow more efficient filtering of contact attempts.
Accordingly, it would be desirable to provide methods, devices, systems and software that would avoid the afore-described problems and drawbacks by, for example, capturing and analyzing communication history obtained from the Web to build enhanced social communication services.